Auto-learned hierarchy algorithms are described in commonly assigned U.S. Pat. No. 7,272,518, titled “Automated Hierarchy Classification in Utility Monitoring Systems,” issued Sep. 18, 2007, which automatically determine a hierarchical arrangement of monitoring devices within a utility system, typically a radial-fed utility system having a main source of a utility, and in commonly assigned U.S. patent application Ser. No. 12/151,309, filed May 6, 2008, titled “Automated Hierarchical Classification for Utility Systems with Multiple Sources,” which describes an automated method for determining a hierarchy of a utility system that includes more than one source, wherein the automated method can differentiate among various types of multiple-source utility models.
Having a thorough knowledge of a utility system's layout is essential to understanding and characterizing the utility system; however, utility meters typically only provide discrete utility operating parameters with no context for the data. Having hierarchical context for the monitoring system data is a powerful tool that provides many useful benefits including troubleshooting system problems, improving system efficiencies, predicting failures and degradation, locating the source of disturbances and interruptions, and modeling system responses.
Utility monitoring systems typically rely on the user's knowledge of the utility system and the utility monitoring system to put the data in context. Visualizations typically use one-line diagrams to provide hierarchical context for more meaningful configuration, analysis and reporting. Many monitoring systems are extensive, and the ability to provide context is confined to small areas such as monitoring device displays and/or computer displays. With monitoring systems that include large and complex hierarchies of meters it can be difficult to obtain a quick concise picture of where energy is being consumed within an enterprise. The problem is compounded as more devices are included in the hierarchy.
Graphical representations of information and parameters in utility monitoring systems provide the end-user with an intuitive means of understanding multifaceted data. A good graphical representation can allow the end-user to create, edit, view and manage multiple hierarchies of different types so that monitoring system data may be placed in an appropriate context for configuration analysis and reporting.